In response to the above problems and the needs of future development in the defect detection field, we have studied related defect detection scenarios that do not provide training samples of the target defect category, instead of conventional defect detection and classification. Figure.REF  shows the basic process of the zero-sample industrial surface defect detection method, in the training phase, a large number of marked seen defects are used, but there are no unmarked unseen defects, we can identify unseen defects through the semantic attribute description shared by the two. Attributes act as a bridge between the training class and the test class, and make up for the asymmetry between the two in terms of information, making it feasible to complete zero-sample defect detection and classification tasks. In terms of attributes, we consider from the aspects of versatility and accuracy, the word vector extracted by the Glove [1]} is used as an intermediate medium between the image and the category label, shared between the unseen defect and the seen defect, and propose a "Latent Feature Guide Attribute Attention" (LFGAA) network based on semantic feedback to realize the detection effect of unseen defects, thereby replacing the conventional defect detection and classification tasks.
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